Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders

• Published in: Brain (London, England : 1878) Vol. 139; no. 8; pp. 2307 - 2321
• Main Authors: Zhang, Jie, Cheng, Wei, Liu, Zhaowen, Zhang, Kai, Lei, Xu, Yao, Ye, Becker, Benjamin, Liu, Yicen, Kendrick, Keith M., Lu, Guangming, Feng, Jianfeng
• Format: Journal Article
• Language: English
• Published: England 01.08.2016
• Subjects: Adolescent; Adult; Attention Deficit Disorder with Hyperactivity - diagnostic imaging; Attention Deficit Disorder with Hyperactivity - physiopathology; Autistic Disorder - diagnostic imaging; Autistic Disorder - physiopathology; Child; Diffusion Tensor Imaging - methods; Electroencephalography - methods; Functional Neuroimaging - methods; Humans; Magnetic Resonance Imaging - methods; Multimodal Imaging; Schizophrenia - diagnostic imaging; Schizophrenia - physiopathology; Time Factors; Young Adult; temporal variability; brain flexibility and adaptability; resting-state functional MRI; functional brain networks; mental disorders
• DOI: 10.1093/brain/aww143
• ISSN: 0006-8950

SEE MATTAR ET AL DOI101093/AWW151 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demonstrate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architecture is modulated by local blood oxygen level-dependent activity and α-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be potentially useful as a predictor for learning and neural rehabilitation.